Abstract
We present CoLe, a model for cooperative agents for mining knowledge from heterogeneous data. CoLe allows for the cooperation of different mining agents and the combination of the mined knowledge into knowledge structures that no individual mining agent can produce alone. CoLe organizes the work in rounds so that knowledge discovered by one mining agent can help others in the next round. We implemented a multi-agent system based on CoLe for mining diabetes data, including an agent using a genetic algorithm for mining event sequences, an agent with improvements to the PART algorithm for our problem and a combination agent with methods to produce hybrid rules containing conjunctive and sequence conditions. In our experiments, the CoLe-based system outperformed the individual mining algorithms, with better rules and more rules of a certain quality. From the medical perspective, our system confirmed hypertension has a tight relation to diabetes, and it also suggested connections new to medical doctors.
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References
Fayyad, U., Uthurusamy, R.: Evolving data into mining solutions for insights. Communications of the ACM 45, 28–31 (2002)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, Reading (1989)
Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Proceedings of the 15th International Conference on Machine Learning, pp. 144–151. Morgan Kaufmann, San Francisco (1998)
Quinlan, J.R.: C4.5: Programs for Machine Learning. The Morgan Kaufmann Series in Machine Learning. Morgan Kaufmann, San Francisco (1993)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)
Liu, H., Lu, H., Yao, J.: Toward multidatabase mining: Identifying relevant databases. IEEE Transactions on Knowledge and Data Engineering 13, 541–553 (2001)
Karaffa, M.C.: International Classification of Diseases, 9th Revision, 4th Edition, Clinical Modification. Practice Management Information Corp., Los Angeles (1992)
World Health Organization: International Classification of Diseases, 9th Revision: Basic Tabulation List with Alphabetical Index. World Health Organization, Geneva (1978)
Denzinger, J.: Conflict handling in collaborative search. In: Tessier, Chaudron, Müller (eds.) Conflicting Agents: Conflict Management in Multi-agent Systems, pp. 251–278. Kluwer Academic Publishers, Dordrecht (2000)
Denzinger, J., Fuchs, D.: Cooperation of heterogeneous provers. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI 1999), Stockholm, Sweden. Morgan Kaufmann, San Francisco (1999)
Viktor, H.L., Arndt, H.: Data mining in practice: From data to knowledge using a hybrid mining approach. The International Journal of Computers, Systems and Signals 1, 139–153 (2000)
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© 2005 Springer-Verlag Berlin Heidelberg
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Gao, J., Denzinger, J., James, R.C. (2005). A Cooperative Multi-agent Data Mining Model and Its Application to Medical Data on Diabetes. In: Gorodetsky, V., Liu, J., Skormin, V.A. (eds) Autonomous Intelligent Systems: Agents and Data Mining. AIS-ADM 2005. Lecture Notes in Computer Science(), vol 3505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492870_8
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DOI: https://doi.org/10.1007/11492870_8
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